Method and apparatus for automatic arrhythmia classification with confidence estimation

ABSTRACT

An arrhythmia classification system receives cardiac data from an implantable medical device, performs automatic adjudication of each cardiac arrhythmia episode indicated by the cardiac data, and generates episode data representative of information associated with the episode. The episode data include at least an episode classification resulting from the automatic adjudication of the episode and a confidence level in the episode classification. In one embodiment, the episode data further include key features rationalizing the automatic adjudication of the episode.

CLAIM OF PRIORITY

This application claims the benefit of priority under 35 U.S.C. §119(e)of Mahajan et al., U.S. Provisional Patent Application Ser. No.61/625,471, entitled “METHOD AND APPARATUS FOR AUTOMATIC ARRHYTHMIACLASSIFICATION WITH CONFIDENCE ESTIMATION”, filed on Apr. 17, 2012,which is herein incorporated by reference in its entirety.

TECHNICAL FIELD

This document relates generally to cardiac rhythm management systems andparticularly to an arrhythmia adjudication system that classifiescardiac arrhythmia episodes and generates a confidence level in theclassification of each of the arrhythmia episodes.

BACKGROUND

The heart is the center of a person's circulatory system. The leftportions of the heart, including the left atrium (LA) and left ventricle(LV), draw oxygenated blood from the lungs and pump it to the organs ofthe body to provide the organs with their metabolic needs for oxygen.The right portions of the heart, including the right atrium (RA) andright ventricle (RV), draw deoxygenated blood from the body organs andpump it to the lungs where the blood gets oxygenated. These mechanicalpumping functions are accomplished by contractions of the heart. In anormal heart, the sinoatrial (SA) node, the heart's natural pacemaker,generates electrical impulses, called action potentials, that propagatethrough an electrical conduction system to various regions of the heartto cause the muscular tissues of these regions to depolarize andcontract. The electrical conduction system includes, in the order bywhich the electrical impulses travel in a normal heart, internodalpathways between the SA node and the atrioventricular (AV) node, the AVnode, the His bundle, and the Purkinje system including the right bundlebranch (RBB, which conducts the electrical impulses to the RV) and theleft bundle branch (LBB, which conducts the electrical impulses to theLV). More generally, the electrical impulses travel through an AVconduction pathway to cause the atria, and then the ventricles, tocontract.

Tachyarrhythmia occurs when the heart contracts at a rate higher than anormal heart rate. Tachyarrhythmia generally includes ventriculartachyarrhythmia (VT) and supraventricular tachyarrhythmia (SVT). VToccurs, for example, when a pathological conduction loop forms in theventricles through which electrical impulses travel circularly withinthe ventricles, or when a pathologically formed electrical focusgenerates electrical impulses from the ventricles. SVT includesphysiological sinus tachyarrhythmia and pathologic SVTs. Thephysiological sinus tachyarrhythmia occurs when the SA node generatesthe electrical impulses at a particularly high rate. A pathologic SVToccurs, for example, when a pathologic conduction loop forms in anatrium. Fibrillation occurs when the heart contracts at atachyarrhythmia rate with an irregular rhythm. Ventricular fibrillation(VF), as a ventricular arrhythmia with an irregular conduction, is alife threatening condition requiring immediate medical treatment such asventricular defibrillation. Atrial fibrillation (AF), as a SVT with anirregular rhythm, though not directly life threatening, also needsmedical treatment such as atrial defibrillation to restore a normalcardiac function and to prevent the deterioration of the heart.

Implantable cardioverter-defibrillators (ICDs) are used to treattachyarrhythmias, including fibrillation. To deliver an effectivecardioversion-defibrillation therapy, an ICD automatically classifieseach tachyarrhythmia by its type and/or origin. One example ofarrhythmia classification performed by an ICD is morphology-basedclassification that determines the origin of a detected tachyarrhythmiaepisode by analyzing a correlation between morphological features of acardiac signal sensed during the tachyarrhythmia episode andmorphological features of a template signal sensed during a known typerhythm. However, morphological changes of a cardiac signal during atachyarrhythmia episode may differ from patient to patient, as well asfrom time to time. Additionally, variations in the morphologicalfeatures may also attribute to changes in other physiological factors.Therefore, there is a need for analyzing the ICD's classification ofeach tachyarrhythmia episode to allow for accurate diagnosis of thepatient's conditions and evaluation of the ICD's performance.

SUMMARY

An arrhythmia classification system receives cardiac data from animplantable medical device, performs automatic adjudication of eachcardiac arrhythmia episode indicated by the cardiac data, and generatesepisode data representative of information associated with the episode.The episode data include at least an episode classification resultingfrom the automatic adjudication of the episode and a confidence level inthe episode classification. In one embodiment, the episode data furtherinclude key features rationalizing the automatic adjudication of theepisode.

In one embodiment, a system is configured to be communicatively coupledto an implantable medical device. The implantable medical device sensesone or more cardiac signals indicative of one or more arrhythmiaepisodes and produces cardiac data representative of the one or morecardiac signals. The system includes an arrhythmia analysis circuit, amemory circuit, and a user interface. The arrhythmia analysis circuit isconfigured to receive cardiac data transmitted from the implantablemedical device, perform automatic adjudication of each episode of one ormore arrhythmia episodes using the cardiac data, and generate episodedata representative of information associated with the episode. Theepisode data include an episode classification resulting from theautomatic adjudication of the episode and a confidence level in theepisode classification. The memory circuit is configured to store dataincluding the cardiac data and the episode data. The user interfaceincludes a presentation device configured to present the informationassociated with the each episode.

In one embodiment, a method for classifying cardiac arrhythmias isprovided. Cardiac data representative of the one or more cardiac signalssensed by an implantable medical device are received. The one or morecardiac signals are indicative of one or more arrhythmia episodes.Automatic adjudication of each episode of one or more arrhythmiaepisodes is performed using the cardiac data. Episode datarepresentative of information associated with the episode are generated.The episode data include an episode classification resulting from theautomatic adjudication of the episode and a confidence level in theepisode classification. The cardiac data and the episode data are storedin a memory device. The information associated with the episode ispresented to a user such as a physician or other caregiver.

This Summary is an overview of some of the teachings of the presentapplication and not intended to be an exclusive or exhaustive treatmentof the present subject matter. Further details about the present subjectmatter are found in the detailed description and appended claims. Otheraspects of the invention will be apparent to persons skilled in the artupon reading and understanding the following detailed description andviewing the drawings that form a part thereof. The scope of the presentinvention is defined by the appended claims and their legal equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate generally, by way of example, variousembodiments discussed in the present document. The drawings are forillustrative purposes only and may not be to scale.

FIG. 1 is an illustration of an embodiment of a cardiac rhythmmanagement (CRM) system including an implantable medical device and anexternal system and portions of an environment in which the CRM systemoperates.

FIG. 2 is a block diagram illustrating an embodiment of portions of acircuit of the implantable medical device and portions of a circuit ofthe external system.

FIG. 3 is a block diagram illustrating an embodiment of the externalsystem.

FIG. 4 is a block diagram illustrating an embodiment of portions of anarrhythmia classification system in the external system.

FIG. 5 is a block diagram illustrating an embodiment of an arrhythmiaanalysis circuit of the arrhythmia classification system.

FIG. 6 is a flow chart illustrating an embodiment of a method forclassifying cardiac arrhythmias.

FIG. 7 is a flow chart illustrating an embodiment of a method forselecting machine learning algorithms for performing automaticarrhythmia adjudication.

FIG. 8 is a flow chart illustrating an embodiment of a method forgenerating episode data for each arrhythmia episode.

FIG. 9 is a flow chart illustrating an embodiment of a method ofdecision tree learning.

FIG. 10 is an illustration of an example of a display screen presentinginformation associated with an arrhythmia episode.

FIG. 11 includes graphs illustrating an example of various factorsconsidered in selecting machine learning algorithms for the automaticarrhythmia adjudication.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which is shown byway of illustration specific embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that the embodiments may be combined, or that otherembodiments may be utilized and that structural, logical and electricalchanges may be made without departing from the spirit and scope of thepresent invention. The following detailed description provides examples,and the scope of the present invention is defined by the appended claimsand their legal equivalents.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one. In this document, the term“or” is used to refer to a nonexclusive or, unless otherwise indicated.Furthermore, all publications, patents, and patent documents referred toin this document are incorporated by reference herein in their entirety,as though individually incorporated by reference. In the event ofinconsistent usages between this documents and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

It should be noted that references to “an”, “one”, or “various”embodiments in this disclosure are not necessarily to the sameembodiment, and such references contemplate more than one embodiment.

This document discusses, among other things, an external systemcommunicating with an implantable medical device and retrospectivelyadjudicating arrhythmia episodes using data acquired and recorded by theimplantable medical device. In various embodiments, the implantablemedical device includes an implantable cardioverter-defibrillator (ICD).Automatic arrhythmia adjudication algorithms have been used toretrospectively classify arrhythmia episodes recorded by an ICD. Due tothe complicated nature of cardiac signals representative of thesearrhythmia episodes, sometimes physicians disagree on the classificationof an arrhythmia episode. Such disagreements are also reflected in theperformance of automatic arrhythmia adjudication algorithms whenexecuted by the external system. In addition to providing theclassification, the present external system provides its user, such as aphysician or other caregiver, with information indicative of confidencein the classification and/or justification of the classification.Examples of such information include a confidence level associated withthe classification, potential alternative classification, and/orinformation explaining rationale behind the classification decision.

FIG. 1 is an illustration of an embodiment of a cardiac rhythmmanagement (CRM) system 100 and portions of an environment in whichsystem 100 operates. CRM system 100 includes an implantable medicaldevice (IMD) 105 that is electrically coupled to a heart throughimplantable leads 110, 115, and 125. An external system 190 communicateswith IMD 105 via a telemetry link 185.

IMD 105 includes a hermetically sealed can housing an electronic circuitthat senses physiological signals and delivers therapeutic electricalpulses. The hermetically sealed can also functions as an electrode(referred to as “the can electrode” hereinafter) for sensing and/orpulse delivery purposes. IMD 105 senses one or more cardiac signalsindicative of one or more arrhythmia episodes and generates cardiac datarepresentative of the one or more cardiac signals. In one embodiment,IMD 105 includes a pacemaker that delivers cardiac pacing therapies. Inanother embodiment, IMD 105 includes the pacemaker and acardioverter/defibrillator that delivers cardioversion/defibrillationtherapies. In various embodiments, IMD 105 includes one or more devicesselected from monitoring devices and therapeutic devices such as thepacemaker, the cardioverter/defibrillator, a neurostimulator, a drugdelivery device, and a biological therapy device. In one embodiment, thepacemaker provides for CRT.

Lead 110 is a right atrial (RA) pacing lead that includes an elongatelead body having a proximal end 111 and a distal end 113. Proximal end111 is coupled to a connector for connecting to IMD 105. Distal end 113is configured for placement in the RA in or near the atrial septum. Lead110 includes an RA tip electrode 114A, and an RA ring electrode 114B. RAelectrodes 114A and 114B are incorporated into the lead body at distalend 113 for placement in or near the atrial septum, and are eachelectrically coupled to IMD 105 through a conductor extending within thelead body. RA tip electrode 114A, RA ring electrode 114B, and/or the canelectrode allow for sensing an RA electrogram indicative of RAdepolarizations and delivering RA pacing pulses.

Lead 115 is a right ventricular (RV) pacing-defibrillation lead thatincludes an elongate lead body having a proximal end 117 and a distalend 119. Proximal end 117 is coupled to a connector for connecting toIMD 105. Distal end 119 is configured for placement in the RV. Lead 115includes a proximal defibrillation electrode 116, a distaldefibrillation electrode 118, an RV tip electrode 120A, and an RV ringelectrode 120B. Defibrillation electrode 116 is incorporated into thelead body in a location suitable for supraventricular placement in theRA and/or the superior vena cava (SVC). Defibrillation electrode 118 isincorporated into the lead body near distal end 119 for placement in theRV. RV electrodes 120A and 120B are incorporated into the lead body atdistal end 119. Electrodes 116, 118, 120A, and 120B are eachelectrically coupled to IMD 105 through a conductor extending within thelead body. Proximal defibrillation electrode 116, distal defibrillationelectrode 118, and/or the can electrode allow for delivery ofcardioversion/defibrillation pulses to the heart. RV tip electrode 120A,RV ring electrode 120B, and/or the can of IMD 105 allow for sensing anRV electrogram indicative of RV depolarizations and delivering RV pacingpulses. In various embodiments, proximal defibrillation electrode 116and/or distal defibrillation electrode 118 may also be used for sensingthe RV electrogram.

Lead 125 is a left ventricular (LV) coronary pacing lead that includesan elongate lead body having a proximal end 121 and a distal end 123.Proximal end 121 is coupled to a connector for connecting to IMD 105.Distal end 123 is configured for placement in the coronary vein. Lead125 includes an LV tip electrode 128A, a distal LV ring electrode 128B,and two proximal LV ring electrodes 128C and 128D. The distal portion oflead 125 is configured for placement in the coronary sinus and coronaryvein such that LV electrodes 128A and 128B are placed in the coronaryvein, and LV electrodes 128C and 128D are placed in or near the coronarysinus. LV electrodes 128A and 128B are incorporated into the lead bodyat distal end 123 and are each electrically coupled to IMD 105 through aconductor extending within the lead body. LV tip electrode 128A, distalLV ring electrode 128B, proximal LV ring electrode 128C, proximal LVring electrode 128D, and/or the can electrode allow for sensing an LVelectrogram indicative of LV depolarizations and delivering LV pacingpulses.

Electrodes from different leads may also be used to sense an electrogramor deliver pacing or cardioversion/defibrillation pulses. For example,an electrogram may be sensed using an electrode selected from RVelectrode 116, 118, and 120A-B and another electrode selected from LVelectrode 128A-D. The lead configuration including RA lead 110, RV lead115, and LV lead 125 is illustrated in FIG. 1 by way of example and notby way of restriction. Other lead configurations may be used, dependingon monitoring and therapeutic requirements. For example, additionalleads may be used to provide access to additional cardiac regions, andleads 110, 115, and 125 may each include more or fewer electrodes alongthe lead body at, near, and/or distant from the distal end, depending onspecified monitoring and therapeutic needs. In various embodiments, IMD105 senses the one or more cardiac signals using any combination ofelectrodes, such as those illustrated in FIG. 1, that is suitable fordetection and classification of the one or more arrhythmia episodes.

External system 190 allows for programming of IMD 105 and receivessignals acquired by IMD 105. In one embodiment, external system 190includes a programmer. In another embodiment, external system 190includes a patient monitoring system such as the system discussed belowwith reference to FIG. 3. In one embodiment, telemetry link 185 is aninductive telemetry link. In an alternative embodiment, telemetry link185 is a far-field radio-frequency telemetry link. Telemetry link 185provides for data transmission from IMD 105 to external system 190. Thismay include, for example, transmitting real-time physiological dataacquired by IMD 105, extracting physiological data acquired by andstored in IMD 105, extracting therapy history data stored in IMD 105,and extracting data indicating an operational status of IMD 105 (e.g.,battery status and lead impedance). The physiological data include thecardiac data representative of the one or more cardiac signals.Telemetry link 185 also provides for data transmission from externalsystem 190 to IMD 105. This may include, for example, programming IMD105 to acquire physiological data, programming IMD 105 to perform atleast one self-diagnostic test (such as for a device operationalstatus), programming IMD 105 to run a signal analysis algorithm (such asan algorithm implementing the tachyarrhythmia detection method discussedin this document), and programming IMD 105 to deliver pacing and/orcardioversion/defibrillation therapies.

External system 190 includes an arrhythmia classification system 130that performs automatic adjudication of arrhythmia episodes using thecardiac data acquired by and telemetered from IMD 105. In variousembodiments, arrhythmia classification system 130 generates episode datafor each arrhythmia episode. The episode data includes datarepresentative of information associated with the arrhythmia episode,including an episode classification resulting from the automaticadjudication of each arrhythmia episode and a confidence level in theepisode classification. In various embodiments, arrhythmiaclassification system 130 also generates episode features including keyfeatures rationalizing the automatic adjudication of the arrhythmiaepisode. In various embodiments, episode information including theepisode classification, the confidence level in the episodeclassification, and the key features rationalizing the automaticadjudication of the arrhythmia episode are presented to the user.

The circuit of CRM system 100 may be implemented using a combination ofhardware and software. In various embodiments, each element of IMD 105and external system 190, as illustrated in FIGS. 1-5, including itsvarious embodiments, may be implemented using an application-specificcircuit constructed to perform one or more particular functions or ageneral-purpose circuit programmed to perform such function(s). Such ageneral-purpose circuit includes, but is not limited to, amicroprocessor or portions thereof, a microcontroller or portionsthereof, and a programmable logic circuit or portions thereof.

FIG. 2 is a block diagram illustrating an embodiment of portions of acircuit of IMD 205 and portions of a circuit of an external system 290.IMD 205 represents an embodiment of IMD 105 and includes a sensingcircuit 232, a defibrillation circuit 234, an implant control circuit236, and an implant telemetry circuit 238. In one embodiment, IMD 205 isan ICD. Sensing circuit 232 includes a rate channel 240 and a morphologychannel 242. Rate channel 240 senses a regional cardiac signal throughelectrodes 250A and 250B for use in heart beat detection. Morphologychannel 242 senses a global cardiac signal through electrodes 250C and250D for use in morphological analysis. In one embodiment, rate channel240 senses a regional ventricular electrogram through an RV tipelectrode such as electrode 120A and an RV coil electrode such aselectrode 118, and morphology channel 242 senses a global ventricularelectrogram through the RV coil electrode and an SVC coil electrode suchas electrode 116. In this embodiment, electrode 250A is the RV tipelectrode, electrodes 250B and 250C are the same RV coil electrode, andelectrode 250D is the SVC coil electrode. In one embodiment, the SVCcoil electrode is electrically connected to the can electrode.Defibrillation circuit 234 includes a shock channel 244 to delivercardioversion/defibrillation pulses (shocks). In the illustratedembodiment, shock channel 244 delivers the shocks using the same pair ofelectrodes as used by morphology channel 242 (so the “morphologychannel” is also referred to as the “shock channel”). In an alternativeembodiment, a single cardiac signal is sensed for use in heart ratedetection and morphology analysis, such as through electrodes 250C and250D. While this alternative embodiment eliminates the need for sensingtwo cardiac signals, the embodiment as illustrated in FIG. 2 providesfor an easier heart beat detection. Implant control circuit 236 controlsthe operation of IMD 205 including the sensing of the one or morecardiac signals and the delivery of the shocks. Implant telemetrycircuit 238 supports the functions of telemetry link 185, includingtransmitting the cardiac data from IMD 205 to external system 290.

External system 290 represents an embodiment of external system 190 andincludes arrhythmia classification system 130, an external telemetrycircuit 248, and a user interface 254. Implant telemetry circuit 248supports the functions of telemetry link 185, including receiving thecardiac data transmitted from IMD 205. User interface 254 includes auser input device 256 and a presentation device 258. User input device256 receives various commands and parameters from the user forcontrolling operations of IMD 205 and external system 290. Presentationdevice 258 presents various patient and device information including theepisode information generated by arrhythmia classification system 130.

FIG. 3 is a block diagram illustrating an embodiment of an externalsystem 390. External system 390 represents an embodiment of externalsystem 290. In the illustrated embodiment, external system 390 is apatient management system including an external device 360, atelecommunication network 362, and a remote device 364. External device360 is to be placed within the vicinity of IMD 205 and includes externaltelemetry circuit 248 to communicate with IMD 205 via telemetry link185. Remote device 364 is in one or more remote locations andcommunicates with external device 360 through network 362, thus allowingthe user to monitor and treat a patient from a distant location and/orallowing access to various treatment resources from the one or moreremote locations. In one embodiment, after the implantation of IMD 205,external system 390 allows the user to adjust settings of IMD 205 andmonitor the patient using data acquired by IMD 205, including thecardiac data.

In various embodiments, external system 390 is a computer-based ormicroprocessor-based system. In various embodiments, arrhythmiaclassification system 130 is distributed in external device 360, remotedevice 364, or both external device 360 and remote device 364. Invarious embodiments, either one or both of external device 360 andremote device 364 include a user interface such as user interface 254.

FIG. 4 is a block diagram illustrating an embodiment of portions of anarrhythmia classification system 430, which represents an embodiment ofarrhythmia classification system 130. Arrhythmia classification system430 includes an arrhythmia analysis circuit 470, a memory circuit 472,and a user interface 454.

Arrhythmia analysis circuit 470 receives the cardiac data transmittedfrom IMD 205, performs automatic adjudication of each episode of one ormore arrhythmia episodes using the cardiac data, and generates episodedata representative of information associated with the each episode. Theinformation associated with the each episode includes an episodeclassification resulting from the automatic adjudication of the eachepisode and a confidence level in the episode classification. Theepisode classification is a classification for the each episode thatindicates a type and an origin of the each episode. In one embodiment,the information associated with the each episode further includesepisode features including key features rationalizing the automaticadjudication of the each episode. Memory circuit 472 stores dataincluding the cardiac data and the episode data. User interface 454represents an embodiment of user interface 254 and includes user inputdevice 256 and a presentation device 458. Presentation device 458presents the information associated with the each episode. In theillustrated embodiment, presentation device 458 includes a displayscreen 474 and a printer 476.

FIG. 5 is a block diagram illustrating an embodiment of an arrhythmiaanalysis circuit 570. Arrhythmia analysis circuit 570 represents anembodiment of arrhythmia analysis circuit 470 and includes an automaticarrhythmia adjudicator 580, a confidence analyzer 586, and arationalization module 588. Automatic arrhythmia adjudicator 580determines the episode classification for each arrhythmia episode usingthe cardiac data by executing a plurality of adjudication algorithms. Asillustrated in FIG. 5, automatic arrhythmia adjudicator 580 includes aplurality of adjudication modules 582 and an episode classifier 584.Adjudication modules 582 each determine a voting classification for theepisode by executing an adjudication algorithm of the plurality ofadjudication algorithms. Episode classifier 584 determines the episodeclassification for the episode using the voting classifications producedby executing the plurality of adjudication algorithms. The adjudicationalgorithms are each selected from available machine learning algorithmseach implementing a tachyarrhythmia adjudication algorithm. In variousembodiments, machine learning algorithms known to produce reasonablyaccurate results in arrhythmia classification are selected to beincluded in the plurality of adjudication algorithms. Some examples ofsuch machine learning algorithms include support vector machine (SVM),decision tree learning, Bayesian machine learning, and outlier detection(such as Mahalanobis distance-based outlier detection). Examples ofsuitable machine learning algorithms are also discussed in Ian H.Witten, Eibe Frank, and Mark A. Hall, Data Mining: Practical MachineLearning Tools and Techniques. Third Edition, Burlington, Mass.: MorganKaufmann, 2011. An example of decision tree learning is discussed belowwith reference to FIG. 9. An example of automatic arrhythmia adjudicator580 is discussed in U.S. Patent Application Publication No. US2010/0280841 A1, entitled “ADJUDICATION OF ARRHYTHMIA EPISODE DATASYSTEMS AND METHODS”, assigned to Cardiac Pacemakers, Inc., which isincorporated herein by reference in its entirety. Confidence analyzer586 determines the confidence level in the episode classification usingthe voting classifications. The confidence level depends on consistencyamong the voting classifications produced by executing the plurality ofadjudication algorithms.

In one embodiment, episode classifier 584 determines the episodeclassification for the episode using one or more primary votingclassifications produced by executing one or more primary adjudicationalgorithms, and confidence analyzer 586 determines the confidence levelin the episode classification using one or more secondary votingclassifications produced by one or more secondary adjudicationalgorithms. The one or more primary adjudication algorithms are selectedfrom the plurality of adjudication algorithms, and the one or moresecondary adjudication algorithms are selected from the remainingalgorithms of the plurality of adjudication algorithms. In variousembodiments, the one or more primary adjudication algorithms include oneor more machine learning algorithms known to produce acceptable resultsin implementing the tachyarrhythmia adjudication algorithm. Theconfidence level is proportional to the number of votingclassification(s) produced by the one or more secondary adjudicationalgorithms that is(are) consistent with the episode classification. Inone embodiment, episode classifier 584 determines the episodeclassification using the voting classification produced by one primaryadjudication algorithm, such as one implemented by support vectormachine using radial basis function (SVM-RBF). Confidence analyzer 586determines the confidence level in the episode classification usingsecondary voting classifications produced by a plurality of secondaryadjudication algorithms. The amount (number) of machine learningalgorithms to be included in the plurality of secondary algorithms isdetermined based on one or more estimated measures of accuracy in theautomatic adjudication and an estimated potential need for manualadjudication by the user, as further discussed below. In anotherembodiment, episode classifier 584 determines the episode classificationbased on a majority voting using the primary voting classificationsproduced by a plurality of primary adjudication algorithms. Confidenceanalyzer 586 determines the confidence level in the episodeclassification using the secondary voting classifications produced by aplurality of secondary adjudication algorithms.

In another embodiment, episode classifier 584 determines the episodeclassification for the episode based on a majority voting using selectedvoting classifications produced by executing the plurality ofadjudication algorithms. Confidence analyzer 586 determines theconfidence level in the episode classification also using the selectedvoting classifications. The confidence level is indicative of apercentage of voting classifications consistent with the episodeclassification. The selected voting classification includes all of aspecified number of the voting classifications produced by the pluralityof adjudication algorithms.

In one embodiment, confidence analyzer 586 produces the determinedconfidence level as a ratio or percentage. In other embodiments, theconfidence level as presented to the user is a function of the ratio orpercentage. In one embodiment, confidence analyzer 586 produces thedetermined confidence level as a parameter having discrete or continuousnumerical values. In another embodiment, the confidence level aspresented to the user includes a parameter having a set of discretevalues, such as high and low, or high, medium, and low, that aredetermined using the actual ratio and one or more threshold values.

In various embodiments, the amount (number) of voting classificationsused to determine the episode classification and the confidence level inthe episode classification is determined based on one or more estimatedmeasures of accuracy in the automatic adjudication and an estimatedpotential need for manual adjudication by the user. An arrhythmiaepisode with an episode classification identified with a high confidencelevel is unlikely to be examined by the user and manually adjudicated.An arrhythmia episode with a classification identified with a lowconfidence level needs to be examined by the user, i.e., needs to bemanually adjudicated. Examples of the estimated measures include a riskfactor, a specificity factor, and a service burden factor. The riskfactor is a proportion of arrhythmia episodes with classificationidentified as high confidence classifications that are misclassifiedduring the automatic adjudication to a total number of classifiedarrhythmia episodes. The risk factor indicates the chance of a highconfidence classification that is actually an incorrect classification.The specificity factor is a proportion of misclassified arrhythmiaepisodes with classifications identified as low confidenceclassifications to the total number of classified arrhythmia episodes.The specificity factor indicates the chance of a low confidenceclassification that is actually an incorrect classification. The serviceburden factor is a proportion (e.g., a percentage) of arrhythmiaepisodes with classification identified as low confidenceclassifications to the total number of classified arrhythmia episodes.The service burden factor indicates the chance of an episode beingclassified with a low-confidence level and hence needs to be manuallyadjudicated. Results from an evaluation of these factors, each as afunction of the number of machine learning algorithms included in theautomatic arrhythmia adjudication, are presented in FIG. 11.

Rationalization module 588 generates episode features including keyfeatures rationalizing the automatic adjudication of each arrhythmiaepisode. The key features indicate the basis upon which the episodeclassification results from the automatic arrhythmia adjudication. Inone embodiment, the episode features also include additional featuresthat are not used in the automatic adjudication but may be useful in themanual adjudication of the arrhythmia episode. Examples of the episodefeatures include (1) heart rates during the episode such as atrial rate,ventricular rate, and average atrial and ventricular rates during aspecified number of the fastest beats; (2) atrioventricular (AV) raterelationship during the episode, such as whether the atrial rateapproximately equals the ventricular rate (1:1 tachyarrhythmia) and theleading channel (the channel with the highest rate) when the atrial ratesubstantially differs from the ventricular rate; (3) onset of theepisode, such as whether and when atrial sudden onset is detected,whether and when ventricular sudden onset is detected, whether and whenpremature ventricular contraction (PVC) detected at the onset of theepisode, whether and when the onset of the episode is detected in eachof the atrial and ventricular channels, and whether atrial onset orventricular onset is first detected; (4) rate stability during theepisode, such as ventricular rate stability; and (5) morphology ofcardiac signal(s) during the episode, such as whether change in shockchannel signal morphology is detected and whether a highly variableshock channel signal morphology is detected. In various embodiments, theinformation associated with the each episode, including the episodeclassification, the confidence level, and the episode features, ispresented to the user to allow for review of the automatic adjudicationof each arrhythmia episode by the user and/or manual adjudication ofeach arrhythmia episode by the user. An example of display screen 474presenting such information is illustrated in FIG. 10. In anotherembodiment, arrhythmia analysis circuit 470 or 570 uses a fuzzy logicrule base to provide rationale for classifying the arrhythmia episode.Rationalization module 588 generates information related to decisionmaking leading to the episode classification.

In one embodiment, arrhythmia analysis circuit 470 or 570 includesportions of a processor circuit, such as a microprocessor, amicrocontroller, or a custom integrated circuit, that are programmed toperform the automatic arrhythmia adjudication, confidence leveldetermination, and episode feature determination functions discussed inthis document. Each element of arrhythmia analysis circuit 570 asillustrated in FIG. 5 includes a portion of the processor circuitprogrammed to perform the function of that element as discussed in thisdocument.

FIG. 6 is a flow chart illustrating an embodiment of a method 600 forclassifying cardiac arrhythmias. In one embodiment, method 600 isperformed using CRM system 100, including the various embodiments of itselements discussed in this document. In one embodiment, arrhythmiaclassification system 130, including the various embodiments of itselements discussed in this document, is programmed to perform method600.

At 602, cardiac data are received from an IMD such as an ICD. Thecardiac data are representative of the one or more cardiac signalssensed by the IMD. The one or more cardiac signals are indicative of oneor more arrhythmia episodes of a patient in whom the IMD is implanted.

At 604, automatic adjudication of each episode of one or more arrhythmiaepisodes is performed using the cardiac data. An episode classificationis determined for the episode using the cardiac data by executing aplurality of adjudication algorithms. The episode classification is aclassification for the episode that indicates a type and an origin ofthe episode. The plurality of adjudication algorithms includes machinelearning algorithms each selected to implement a tachyarrhythmiaadjudication algorithm.

At 606, episode data are generated. The episode data are representativeof information associated with the episode, and includes an episodeclassification resulting from the automatic adjudication of the episodeand a confidence level in the episode classification. In one embodiment,the information associated with the episode further includes episodefeatures including key features rationalizing the automatic adjudicationof the each episode. The key features allow the user to manuallyadjudicate the episode, such as when the confidence level in the episodeclassification is low.

At 608, the cardiac data and the episode data are stored in a memorydevice. At 610, the information associated with the episode is presentedto the user. This allows the user to review the patient's conditionsincluding the one or more arrhythmia episodes. In various embodiments,portions of the cardiac data and the episode data are presentedsimultaneously using a display screen. An example of such a presentationis illustrated in FIG. 10.

FIG. 7 is a flow chart illustrating an embodiment of a method 700 forselecting machine learning algorithms and voting classificationsproduced by executing the selected machine learning algorithms fordetermination of the episode classification of an arrhythmia episode andthe confidence level in the episode classification. Method 700 is usedto select the plurality of adjudication algorithms for use in method600.

At 702, suitable machine learning algorithms are identified forimplementing a tachyarrhythmia adjudication algorithm. To automaticallyadjudicate the arrhythmia episode, the identified machine learningalgorithms are each executed to generate a voting classification for thearrhythmia episode. At 704, the amount (number) of votingclassifications (decisions) produced by these machine learningalgorithms to be included in determining the episode classification forthe arrhythmia episode and/or the confidence level in the episodeclassification is determined based on one or more estimated measures ofaccuracy in the automatic arrhythmia adjudication and an estimatedpotential need for manual adjudication by the user. In the illustratedembodiment, the amount of machine learning algorithms is determinedbased on the risk factor, the specificity factor, and the service burdenfactor as discussed above with reference to FIG. 5. At 706, the riskfactor is considered. At 708, the specificity factor is considered. At710, the service burden factor is considered. In various embodiments ofmethod 700, any one or more steps selected from 706, 708, and 710 areperformed. The amount of classifications produced by the machinelearning algorithms to be included in determining the episodeclassification and/or the confidence level in the episode classificationis determined by balancing the performance (accuracy of arrhythmiaclassification) with the cost (system complexity and user time).

FIG. 8 is a flow chart illustrating an embodiment of a method 800 forgenerating episode data for each arrhythmia episode. Method 800represents an embodiment of part of method 600 including steps 604 and606. In one embodiment, arrhythmia analysis circuit 470, including thevarious embodiments of its elements discussed in this document, isprogrammed to perform method 800.

At 802, voting classifications for each arrhythmia episode are eachdetermined by executing an adjudication algorithm of the plurality ofadjudication algorithms. At 804, the episode classification isdetermined by one or more primary voting classifications selected fromthe voting classifications produced by executing the plurality ofadjudication algorithms. At 806, the confidence level in the episodeclassification is determined using one or more secondary votingclassifications selected from the voting classifications produced byexecuting the plurality of adjudication algorithms. In one embodiment,the one or more primary voting classifications and the one or moresecondary voting classifications include one or more votingclassifications produced by the same adjudication algorithm(s) selectedfrom the plurality of adjudication algorithms. In another embodiment,the one or more primary voting classifications and the one or moresecondary voting classifications include voting classifications producedby the different adjudication algorithm selected from the plurality ofadjudication algorithms.

At 808, episode features rationalizing the episode classification aregenerated. In one embodiment, the episode features include features usedto determine the episode classification by executing the plurality ofadjudication algorithms. In one embodiment, the episode features includefeatures allowing for manual adjudication of the episode. In variousembodiments, the episode data are included in the information associatedwith the episode to be stored and presented to the user.

FIG. 9 is a flow chart illustrating an embodiment of a method fordecision tree learning referred to as “one-versus-all” decision tree.The illustrated decision tree is for determining whether a detectedarrhythmia episode is of a particular type designated as “arrhythmiatype A” for illustrative purposes. Three episode parameters (episodeparameters 1, 2, and 3) each indicating one or more characteristics ofarrhythmia type A are determined from one or more cardiac signalsindicative of the detected arrhythmia episode. Episode parameters 1, 2,and 3 each have a predetermined threshold value (thresholds 1, 2, and3). Each episode parameter is compared to its threshold value. Theoutcome of the comparison determines whether the detected arrhythmiaepisode is not an arrhythmia type A (i.e., “other”) or the next episodeis to be compared to its threshold value until all the episodeparameters are compared. Examples of “arrhythmia type A” includeventricular fibrillation (VF), ventricular tachycardia (VT),supraventricular tachyarrhythmia (SVT), atrial fibrillation (AF), atrialflutter (AFL), sinus tachycardia (ST), and atrial tachycardia (AT).Examples of the episode parameters include an atrial rate, a ventricularrate, an onset rate indicating whether the detected arrhythmia episodehas a gradual onset or a sudden onset, a stability parameter indicativeof a degree of ventricular rate variability, and a correlationcoefficient representative of a morphological correlation between awaveform of a cardiac signal during the detected arrhythmia episode anda template waveform being a waveform of the cardiac signal during aknown cardiac rhythm.

FIG. 10 is an illustration of an example of a display screen presentinginformation associated with an arrhythmia episode. In variousembodiments, the display screen is part of an external system forcommunicating with an IMD. One example of such a display screen isdisplay screen 474. In various other embodiments, the display screen ofpart of any computer or computer-based system used to analyze cardiacdata representing cardiac signals indicative of one or more arrhythmiaepisodes.

The presented information associated with an arrhythmia episode asillustrated in FIG. 10 includes:

-   -   intracardiac electrograms:        -   atrial electrogram (“Atrial”);        -   ventricular electrogram sensed though a rate channel            (“Ventricular”); and        -   a ventricular electrogram sensed through shock channel            (morphology channel) (“Shock”);    -   the episode classification (“Classification”);    -   the confidence level in the episode classification        (“Confidence”);    -   the alternative episode classification (“Alternative”) (“None”        value unless the confidence level is low);    -   episode features including key features rationalizing the        automatic adjudication of the each episode (“Other        Information”);        -   heart rates during the episode (“Rate”):            -   atrial rate; and            -   ventricular rate;        -   onset of the episode (Onset”):            -   whether onset as detected in an atrium is sudden; and            -   whether onset as detected in a ventricle is sudden;        -   stability {“Stability”):            -   whether ventricular rate is stable; and        -   morphology (Morphology):            -   whether morphology of the cardiac signal sensed through                the shock channel changes;

Various types of information are presented in FIG. 10 by way of example,and not by way of limitation. In various embodiments, the informationassociated with an arrhythmia episode may include any combination ofsuch information as discussed in this document and any other informationthat is available and interested by the user for the purpose ofperforming monitoring, diagnosis, and/or treatment of the patient.

FIG. 11 includes graphs illustrating an example of various factorsconsidered in selecting machine learning algorithms for automaticadjudication of arrhythmia episodes. The risk, specificity, and serviceburden factors are each evaluated with a plurality of machine learningalgorithms implementing a tachyarrhythmia adjudication algorithm. Thegraph for the risk factors shows that the chance for a high confidenceclassification to be actually an incorrect classification decreases withthe number of machine learning algorithms used. The graph of thespecificity factor shows that the chance of a low confidenceclassification to be actually an incorrect classification increases withthe number of machine learning algorithms used. The graph of the serviceburden factor shows that the percentage of arrhythmia episodes thatneeds to be manually reviewed increases with the number of machinelearning algorithms used. Such graphs provide a basis for determiningthe number of classifications produced by machine learning algorithms tobe included for determining the episode classification for an arrhythmiaepisode and/or the confidence level in the episode classification bybalancing the benefit (accuracy of arrhythmia classification) and cost(system cost and user time for manual analysis).

It is to be understood that the above detailed description is intendedto be illustrative, and not restrictive. Other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the invention should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A system configured to be communicatively coupledto an implantable medical device sensing one or more cardiac signalsindicative of one or more arrhythmia episodes and produce cardiac datarepresentative of the one or more cardiac signals, the systemcomprising: an arrhythmia analysis circuit configured to receive cardiacdata transmitted from the implantable medical device, perform automaticadjudication of each episode of one or more arrhythmia episodes usingthe cardiac data, and generate episode data representative ofinformation associated with the each episode including an episodeclassification resulting from the automatic adjudication of the eachepisode and a confidence level in the episode classification; a memorycircuit configured to store data including the cardiac data and theepisode data; and a user interface including a presentation deviceconfigured to present the information associated with the each episode.2. The system of claim 1, wherein the arrhythmia analysis circuit isconfigured to determine the episode classification for the each episodeand the confidence level in the episode classification using the cardiacdata by executing a plurality of adjudication algorithms.
 3. The systemof claim 2, wherein the arrhythmia analysis circuit comprises anautomatic arrhythmia adjudicator configured to execute the adjudicationalgorithms each being a machine learning algorithm implementing atachyarrhythmia adjudication algorithm.
 4. The system of claim 2,wherein the automatic arrhythmia adjudicator comprises: a plurality ofadjudication modules each configured to determine a votingclassification for the each episode by executing an adjudicationalgorithm of the plurality of adjudication algorithms; and an episodeclassifier configured to determine the episode classification using oneor more primary voting classifications selected from the votingclassifications produced by executing the plurality of adjudicationalgorithms.
 5. The system of claim 4, wherein the arrhythmia analysiscircuit comprises a confidence analyzer configured to determine theconfidence level in the episode classification using one or moresecondary voting classifications selected from the votingclassifications produced by executing the plurality of adjudicationalgorithms, the confidence level indicative of a percentage of the oneor more secondary voting classifications consistent with the episodeclassification.
 6. The system of claim 2, wherein the informationassociated with the each episode further includes episode featuresincluding key features rationalizing the automatic adjudication of theeach episode.
 7. The system of claim 6, wherein the arrhythmia analysiscircuit comprises a rationalization module configured to generateepisode features including features used by the automatic arrhythmiaadjudicator to determine the episode classification for the eachepisode.
 8. The system of claim 7, wherein the rationalization module isconfigured to generate episode features including features allowing formanual adjudication of the each episode.
 9. The system of claim 1,comprising an external telemetry circuit configured to receive thecardiac data from the implantable medical device.
 10. The system ofclaim 9, comprising an external device including the external telemetrycircuit, a remote device including at least the user interface, and acommunication network coupling between the external device and theremote device.
 11. A method for classifying cardiac arrhythmias, themethod comprising: receiving cardiac data representative of the one ormore cardiac signals sensed by an implantable medical device, the one ormore cardiac signals indicative of one or more arrhythmia episodes;performing automatic adjudication of each episode of one or morearrhythmia episodes using the cardiac data; generating episode datarepresentative of information associated with the each episode includingan episode classification resulting from the automatic adjudication ofthe each episode and a confidence level in the episode classification;storing the cardiac data and the episode data in a memory device; andpresenting the information associated with the each episode.
 12. Themethod of claim 11, wherein performing the automatic adjudicationcomprises determining the episode classification for the each episodeand the confidence level in the episode classification using the cardiacdata by executing a plurality of adjudication algorithms.
 13. The methodof claim 12, wherein the plurality of adjudication algorithms comprisesmachine learning algorithms each selected to implement a tachyarrhythmiaadjudication algorithm.
 14. The method of claim 13, comprisingdetermining an amount of machine learning algorithms to be included inthe plurality of adjudication algorithms based on one or more estimatedmeasures of accuracy in the automatic adjudication and an estimatedpotential need for manual adjudication by a user.
 15. The method ofclaim 14, comprising determining the amount of machine learningalgorithms to be included in the plurality of adjudication algorithmsbased on one or more factors selected from: a risk factor being aproportion of arrhythmia episodes with classification identified as highconfidence classifications that are misclassified during the automaticadjudication to a total number of classified arrhythmia episodes; aspecificity factor being a proportion of misclassified arrhythmiaepisodes with classifications identified as low confidenceclassifications to the total number of classified arrhythmia episodes;and a service burden factor being a proportion of arrhythmia episodeswith classification identified as low confidence classifications to thetotal number of classified arrhythmia episodes.
 16. The method of claim12, wherein performing the automatic adjudication comprises: determininga voting classification for the each episode by executing anadjudication algorithm of the plurality of adjudication algorithms; anddetermining the episode classification using one or more primary votingclassifications selected from the voting classifications produced byexecuting the plurality of adjudication algorithms.
 17. The method ofclaim 16, comprising determining the confidence level in the episodeclassification using one or more secondary voting classificationsselected from the voting classifications produced by executing theplurality of adjudication algorithms, the confidence level indicative ofa percentage of the one or more secondary voting classificationsconsistent with the episode classification.
 18. The method of claim 12,wherein the information associated with the each episode furtherincludes episode features including key features rationalizing theautomatic adjudication of the each episode.
 19. The method of claim 18,comprising generating episode features including features used todetermine the episode classification during the automatic adjudicationof the each episode.
 20. The method of claim 19, comprising generatingepisode features including features allowing for manual adjudication ofthe each episode.